Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
PLoS One. 2013 May 10;8(5):e63211. doi: 10.1371/journal.pone.0063211. Print 2013.
For decades, ecologists have measured habitat attributes in the field to understand and predict patterns of animal distribution and abundance. However, the scale of inference possible from field measured data is typically limited because large-scale data collection is rarely feasible. This is problematic given that conservation and management typical require data that are fine grained yet broad in extent. Recent advances in remote sensing methodology offer alternative tools for efficiently characterizing wildlife habitat across broad areas. We explored the use of remotely sensed image texture, which is a surrogate for vegetation structure, calculated from both an air photo and from a Landsat TM satellite image, compared with field-measured vegetation structure, characterized by foliage-height diversity and horizontal vegetation structure, to predict avian density and species richness within grassland, savanna, and woodland habitats at Fort McCoy Military Installation, Wisconsin, USA. Image texture calculated from the air photo best predicted density of a grassland associated species, grasshopper sparrow (Ammodramus savannarum), within grassland habitat (R(2) = 0.52, p-value <0.001), and avian species richness among habitats (R(2)= 0.54, p-value <0.001). Density of field sparrow (Spizella pusilla), a savanna associated species, was not particularly well captured by either field-measured or remotely sensed vegetation structure variables, but was best predicted by air photo image texture (R(2)= 0.13, p-value = 0.002). Density of ovenbird (Seiurus aurocapillus), a woodland associated species, was best predicted by pixel-level satellite data (mean NDVI, R(2)= 0.54, p-value <0.001). Surprisingly and interestingly, remotely sensed vegetation structure measures (i.e., image texture) were often better predictors of avian density and species richness than field-measured vegetation structure, and thus show promise as a valuable tool for mapping habitat quality and characterizing biodiversity across broad areas.
几十年来，生态学家一直在实地测量栖息地属性，以了解和预测动物分布和丰度的模式。然而，从实地测量数据中进行推断的范围通常受到限制，因为大规模的数据收集很少可行。鉴于保护和管理通常需要精细而广泛的数据，这是一个问题。遥感方法的最新进展为在广泛的区域内有效地描述野生动物栖息地提供了替代工具。我们探索了使用遥感图像纹理，它是一种替代植被结构的指标，该指标是从航空照片和 Landsat TM 卫星图像计算得出的，与实地测量的植被结构，其特征是叶高多样性和水平植被结构，以预测美国威斯康星州 Fort McCoy 军事设施草原、热带稀树草原和林地栖息地的鸟类密度和物种丰富度。从航空照片计算得出的图像纹理最好地预测了草原相关物种草地雀（Ammodramus savannarum）在草原栖息地内的密度（R²=0.52，p 值<0.001），以及栖息地间的鸟类物种丰富度（R²=0.54，p 值<0.001）。草原相关物种田雀（Spizella pusilla）的密度并没有特别好地被实地测量或遥感植被结构变量捕捉到，但最好是由航空照片图像纹理预测（R²=0.13，p 值=0.002）。林地相关物种林莺（Seiurus aurocapillus）的密度最好由像素级卫星数据（平均 NDVI，R²=0.54，p 值<0.001）预测。令人惊讶和有趣的是，遥感植被结构测量值（即图像纹理）通常比实地测量的植被结构更好地预测鸟类密度和物种丰富度，因此有望成为在广泛区域内绘制栖息地质量和描述生物多样性的有价值工具。
